Routing of contacts based on predicted escalation time

ABSTRACT

Systems and methods that employ contact escalation periods as criterion for managing routing procedures of a contact center. A prediction component can predict when a customer is likely to escalate a contact that is forwarded to a contact center, and hence facilitate resource matching based on such prediction. Accordingly, proactive and anticipatory contact interaction is enabled, wherein routing of contacts occur in-part based on predicted likelihood of escalations.

BACKGROUND

The Internet and multimedia applications are becoming increasinglypopular across the world, and there exists a significant momentum forindustry to build and deploy efficient Internet-based contact centers.In general, a contact center (also known as a call center or customerinteraction center) is a centralized office, which operates as part ofan organization's customer relationship management (CRM). For example,the contact center can receive and transmit large volume of requests viavarious communication channels (e.g., telephone, fax, live chat, e-mail,letter, and the like.) The contact center can further administerincoming product support or information inquiries from clients or users,for example. Likewise, outgoing calls for telemarketing, client issues,product services, and the like can also be administered by the contactcenter.

Accordingly, a contact center represents a central point for anorganization from which customer contacts can be managed. The contactcenter can consist of a number of human agents, wherein each agent isassigned to a telecommunication device (e.g., a phone or a computer forconducting email or Internet chat sessions)—which is connected to acentral switch. Such devices enable agents to provide sales, customerservice, or technical support to existing customers or prospects. Thecontact center can be independently operated or networked withadditional centers, and is often linked to a corporate computer network.Voice and data pathways into such centers can further be linked throughtechnologies such as computer telephony integration (CTI).

To this end, contact centers enable routing of company information tocustomers, while tracking their information for accumulation ofmarketing data. Moreover, various enterprises employ contact centers forservicing internal functions. Such can include help desks, retailfinancial support, sales support, and the like. In general, contactcenters can establish “preferred” channels of contacts, for differenttypes of inquiries. Such preferences can typically be based on costsassociated with handling each type of contact. For example, for contactssuch as product service inquiries that typically do not generaterevenue, web based support portal(s) can be designated as the preferredchannel. Likewise, for contacts related to purchase of products, thepreferred channel can be a live human contact center agent viavoice—which enables an opportunity to “up sell” the caller with moreprofitable products/services. Such designations of preferred channels donot necessarily indicate that specific types of service are onlyavailable on these specific channels. Rather, the designations merelyrepresent that the businesses have established preferences based onbalancing costs and revenues, with respect to pairing the services withcontact designations.

Contact centers that respond to incoming contacts are typically referredto as “inbound contact centers”, and contact centers that engage inoutgoing contacts are referred to as “outbound contact centers.” Forexample, in an outbound contact center contacts can occur to encouragesales of a product, provide technical support or supply billinginformation, survey consumer preferences, and the like. In both cases ofinbound and outbound, a contact center operation can include a switchsystem that connects callers to agents.

In an inbound contact center, such switches route incoming callers to aparticular agent in a contact center—or alternatively—if multiplecontact centers are deployed, to a particular contact center for furtherrouting. Likewise, in an outbound contact center that employs telephonedevices, dialers are typically employed in addition to a switch system.The dialer can automatically dial a phone number from a list of phonenumbers, and further determine whether a live person has actually beenreached (as opposed to reaching an answering machine). Subsequently, andwhen the dialer obtains a live caller, the switch system routes thecaller to a particular agent in the contact center.

To this end, routing technologies have been developed to optimize callerexperience. For example, a telephone system can equalize caller waitingtimes across multiple telephone switches, regardless of generalvariations in performance that can exist among those switches.

Typically, the contact center can set up a queue of incoming callers andpreferentially route longest-waiting callers to agents that becomeavailable periodically. Such pattern of routing contacts to either thefirst available agent (or the longest-waiting agent) is referred to as“round-robin” contact routing. In general, attempts have been made toimprove upon such standards.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview of the claimed subject matter. It is intended toneither identify key or critical elements of the claimed subject matternor delineate the scope thereof. Its sole purpose is to present someconcepts in a simplified form as a prelude to the more detaileddescription that is presented later.

Various aspects of the subject innovation predict when a customer islikely to escalate a contact received by a contact center, and employsuch prediction for contact escalation as part of managing routingprocedures (e.g., resource matching) for the contact center. As such,the predicted contact escalation period can be considered as anadditional criteria in creating models (e.g., queuing models, Markovchains) for resource matching of the contact center (e.g., models thatcan be built across inputs received by customers of the contact center.)Hence, proactive and anticipatory contact interaction is enabled,wherein inbound or incoming contact prioritization can occur in-partbased on predicted time for likelihood of contact escalation.

In one aspect, a prediction component can initially predict likelihoodfor a contact escalation from a less expensive resource usage of thecontact center, to a more expensive resource thereof. Subsequently, andbased on such likelihood of escalation, priorities can then besatisfied, to avoid or mitigate actual occurrence of the contactescalation. Moreover, the prediction component can predict timeperiod(s) associated with the likelihood of escalation, in part based oncontextual data such as: 1) subject matter of the contact; 2) charactertraits/behavior of the specific customer or user who initiates suchcontact (when available) or based on demographic model when priorbehavior for a specific individual is not available; 3) contactenvironment (e.g., type of channel employed, location), and the like.The contextual data can further be supplied by data banks, such as datamined by third parties, for example.

Moreover, the subject innovation can be employed as part of monitoring asocial network arrangement—wherein subject matter of a contact isinitially presented to the social network (e.g., peer-to-peer group),and before the customer actually forwards such contact to the contactcenter. By monitoring such social network, the prediction component canfurther consider possible transfer of the contact from the socialnetwork to the contact center, as part of predicting the likelihood forcontact escalation.

In a related aspect, predicted escalation periods can be dynamicallyupdated in real-time, while the contact is being processed by thecontact center. For example, if escalation becomes substantially likelyor inevitable—(e.g., due to lack of resources to be deployed to managethe contact)—remedial measures that can change predicted escalationperiods can be taken in an attempt to appease the customer. In oneaspect, the remedial measures can represent procedures that can persuadethe contact to maintain a current contact channel and hence notescalate. Likewise, rewards or other procedures can be employed (e.g.,on all channels), which can be deemed indicative that progress is made(e.g., a voice that states “your call is important to us, please stay onthe line.”) Hence, predicted contact escalation periods can bedynamically updated in real-time.

According to a further aspect, the prediction component can furtherassign threshold values in form of probability functions, to thecontact—for anticipating a likelihood of when an escalation for suchcontact can in fact occur. Hence, workforce management can beefficiently customized to facilitate required staffing/agent surplus, bymitigating contact escalation (e.g., employing fewer resources that aremore costly, than the resources employed prior to contact escalation.)The prediction component can further facilitate supplying a response,which occurs prior to expiration of a time period(s) that is associatedwith such likelihood of contact escalation.

In accordance with a methodology of the subject innovation, initiallyfeature extraction can be performed on a contact received by the contactcenter—wherein information associated with the contact, such as topic ofrequest, customer information and the like can be obtained. Suchinformation extracted from the contact can be represented in form ofvectors for interaction with a model that probabilistically predictscontact escalation point (e.g., training of a Markov model). A customerhistory can further be created, wherein the Markov model can then beemployed to compute a likelihood of contact escalation—(e.g., percentagechance that a customer will follow up on the initial request throughanother channel, and/or on same channel with different destination) thatis considered to be more expensive than the original channel, fromstandpoint of the contact center)—as a function of character traits ofcustomer, or cost of resources associated with the contact center.

The following description and the annexed drawings set forth in detailcertain illustrative aspects of the claimed subject matter. Theseaspects are indicative, however, of but a few of the various ways inwhich the principles of such matter may be employed and the claimedsubject matter is intended to include all such aspects and theirequivalents. Other advantages and novel features will become apparentfrom the following detailed description when considered in conjunctionwith the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an exemplary system that predictswhen a customer is likely to escalate a contact that is forwarded to acontact center, in accordance with an aspect of the subject innovation.

FIG. 2 illustrates a particular prediction component that employs theescalation prediction as part of managing routing procedures (e.g.,resource matching) for the contact center, according to an aspect of thesubject innovation.

FIG. 3 illustrates a further exemplary aspect of a prediction component,in accordance with an aspect of the subject innovation.

FIG. 4 illustrates an evaluation component in conjunction with queuingmodels that can employ contact escalation periods as part of routingprocedures.

FIG. 5 illustrates another aspect of the subject innovation that can beemployed as part of monitoring social network arrangements.

FIG. 6 illustrates methodology of predicting when a customer is likelyto escalate a contact, in accordance with an aspect of the subjectinnovation.

FIG. 7 illustrates a related methodology of employing contact escalationperiods as part of routing management of a contact center.

FIG. 8 illustrates an inference component that facilitates predictingwhen a customer is likely to escalate a contact received by a contactcenter, in accordance with an aspect of the subject innovation.

FIG. 9 illustrates a particular system for predicting when a customer islikely to escalate a contact according to an aspect of the subjectinnovation.

FIG. 10 illustrates an exemplary operating environment for implementingvarious aspects of the subject innovation.

FIG. 11 illustrates a schematic block diagram of a sample computingenvironment with which the subject innovation can interact.

DETAILED DESCRIPTION

The various aspects of the subject innovation are now described withreference to the annexed drawings, wherein like numerals refer to likeor corresponding elements throughout. It should be understood, however,that the drawings and detailed description relating thereto are notintended to limit the claimed subject matter to the particular formdisclosed. Rather, the intention is to cover all modifications,equivalents and alternatives falling within the spirit and scope of theclaimed subject matter.

FIG. 1 illustrates an exemplary communication system 100 that canpredict when a customer 115 is likely to escalate a contact, and furtheremploy such escalation prediction as part of managing routing procedures(e.g., resource matching) for the contact center 110. As illustrated,the telecommunication network cloud 120 can represent a public orsemi-public space on transmission lines that exists between end pointsof a transmission. Data that is transmitted across a WAN can enter suchnetwork from one end point using a standard protocol—wherein the datacan then share space with other data transmissions while entering thetelecommunication network cloud 120, for example. Moreover, whenemerging from the telecommunication network cloud 120—such data can beencapsulated, translated and transported in various ways—includingremaining in the same format as when it entered the telecommunicationnetwork cloud 120. Accordingly, the telecommunication network cloud 120can represent an unpredictable space that the data enters before it isreceived (e.g., typically no two packets will necessarily follow thesame physical path, when data is transmitted in a packet across apacket-switched network.) In this regard, the telecommunication networkcloud 120 can include Public Switched Telephone Network, the Internet,Private data network, a satellite network, and the like.

Moreover, the telecommunication network cloud 120 can include a singlecontact address, (e.g., a telephone number or email address), ormultiple contract addresses, for example. Furthermore, the routingcomponent 130 can represent contact routing hardware and/or softwarethat are designed to facilitate routing contacts in the contact center110. As such, the routing component 130 can route a contact to an agent135 (1 to m, where m is an integer) of the contact center 110. Eventhough the routing component 130 is illustrated as being separate fromthe prediction component 125, is to be appreciated that otherconfigurations are well within the realm of the subject innovation.

Customer 115 can represent an individual or entity that interacts withan enterprise via the contact center 110, for obtaining informationusing any of a variety of platforms or communication channels, such astelephone, e-mail, text based communication (e.g., text messaging,on-line information exchange, instant messaging, short message service)and the like. The contact center 110 can be implemented as part ofhardware and/or software (e.g., for receiving a call fromcustomer/caller 115, communicating with caller 115, coordinatingcommunication with caller 115 and call-center agents 135, and the like.)Furthermore, each call-center agent 135 can be assigned a differentqueue of callers to be processed—wherein the amount of time can beestimated for talking to an agent, and subsequently supplied to eachcaller.

As illustrated, the prediction component 125 can initially predictlikelihood for escalation of a contact (e.g., from a less expensiveresource to a more expensive resource of the contact center.)Subsequently, and based on such likelihood of escalation, priorities canthen be satisfied, in an attempt to avoid or mitigate actual occurrenceof the escalation. As such, the predicted escalation period can beconsidered as an additional criteria in creating models for resourcematching of the contact center 110 (e.g., models that can be builtacross inputs received by customers of the contact center.) Hence,proactive and anticipatory contact interaction is enabled, whereininbound contact prioritization can occur in-part based on predicted timefor likelihood of escalation.

FIG. 2 illustrates a particular prediction component 210 that includesthe routing component 220 as part of managing routing procedures (e.g.,resource matching) for the contact center, according to an exemplaryaspect of the subject innovation. The routing component 220 can applyvarious models to route contacts of the contact center, wherein suchmodels can employ criteria such as; amount of time before a caller cancommunicate with call-center agent, a satisfaction rating; areas ofexpertise of call-center agent; the primary language spoken; location ofcall-center; and the like, for example. To this end, the subjectinnovation introduces the contact escalation period as an additionalcriteria in creating model(s) 225 for resource matching of the contactcenter (e.g., as part of models that can be built across inputs receivedby customers of the contact center.)

As explained earlier, the routing component 220 can further employadditional models based on other criteria such as agent grade data,(e.g., data associated with agent performance for a particular desiredperformance); ranking or scoring a set of agents based on performancefor a particular outcome (e.g., revenue generation, cost, customersatisfaction, or a combination thereof); and other preferential criteriasuch as routing callers to agents based on a performance ranking orscore, for example. Hence, the routing component 220 can receive agentgrades or agent history data and output one or more rankings of agentsbased on one or more desired outcome variables.

In a related aspect, in addition to the criteria of contact escalation,the routing component 220 can include one or more pattern matchingalgorithms, which can operate to compare agent data associated with aset of callers-to-agent data associated with a set of agents anddetermine a suitability score of each caller-agent pair. For example,the routing component 220 can receive caller data and agent data fromvarious databases and output caller-agent pair scores or a ranking ofcaller-agent pairs. As explained earlier, such pattern matching canfurther employ the predicted likelihood for escalation of a contact,from a less expensive resource to a more expensive resource of thecontact center. Subsequently, and based on such likelihood ofescalation, priorities can be satisfied and patterns matched, to avoidor mitigate actual occurrence of the escalation. It is to be appreciatedthat such pattern matching algorithm can employ various neural networkalgorithms, or adaptive algorithms. affinity matching algorithms and thelike—wherein different matching engines can be employed with differentpattern matching algorithms operating on the same or different inputdata, to maximize different output variables; such as maximizingcustomer satisfaction, for example.

It is to be appreciated that escalation of a contact by a customer, isbased on criteria that is typically personal to such customer—(e.g.,subjective criteria that can change from user to user such as; customersatisfaction; customer goals, and the like.) Thus, an escalation ofcontact does not necessarily represent an increase in “severity” ofissues faced by such customer. For example, severity can be increasedvia an increase in specific down production, or adverse impact onmission critical applications of an entity. Yet, such entity as acustomer can escalate contact and demand immediate attention withoutnecessarily experiencing such increased severity issues. Stateddifferently, customers can escalate any contact, for any level ofseverity, at any time.

In contrast, actual occurrence of the contact escalation can beobjectively defined via a cost function for resources of the contactcenter 205. For example, contact escalation and/or follow-up can resultfrom: 1) a user attempting to reach the contact center on an additionalchannel, which is deemed more expensive than the channel originallyemployed for the contact; and/or 2) the user attempting to reach anadditional destination on the same channel, yet the additionaldestination being deemed a more expensive resource than the destinationoriginally intended (e.g., attempt to speak with the owner of thecompany, as opposed to a company representative with a lowerrank/associated hourly cost.) Stated differently, escalation of acontact and/or follow up corresponds to an increased cost of theresource utilized, to resolve issues, or manage/respond to the contact(e.g., “same issue other channel”, or “same issue same channel differentdestination.”) For example, from standpoint of a contact center withmulti-tiered cross-channeled communications, an attempt to follow up ona contact via a real-time telephone call to a live agent can be deemedmore expensive than an e-mail request via the Internet. Likewise,sending an email to a specific manager e-mail box can cause moreexpensive resources to be utilized for creating a response—then sendingan e-mail to the general email box. As such, the escalation can berepresented by abandonment of an initial request—in favor of—anadditional request, which requires a resource of the contact center thatis more expensive than a resource required by the initial request.

To this end, the analysis component 235 can provide a value functionthat evaluates resources of the contact center—while also performingcost-benefit analysis for matching contacts to such resources based onlikelihood of contact escalations. This data can then be presented tothe contact center 205, to determine which contact from which customershould be fulfilled.

The analysis component can continue to compute a value of fulfilling acontact k (e.g., utility of resolving issue about contact k) byanalyzing a difference between a value of such contact fulfillment,versus not resolving contact k within the context. For instance, giventhe value of various resources of the contact center, the analysiscomponent can evaluate:

Value of fulfilling contact k=Utility(fulfilling contact(k))−Utility(notfulfilling contact(k))

Such can further be considered in terms of a threshold value, asrepresented by:

Utility(fulfilling(k)|context cost of resources)>threshold value.

Such threshold value can be predetermined by the contact center 205, tofacilitate the cost-benefit analysis. An example of the threshold rulecan include “Choose contact with 90% likelihood of escalation fromcustomer A, over contact with 10% likelihood of escalation from CustomerB.”

FIG. 3 illustrates a further exemplary aspect of a prediction component310 that can predict time period(s) associated with the likelihood ofescalation, in part based on contextual data such as: 1) subject matterof the contact; 2) character traits/behavior of the customer whoinitiates such contact, 3) contact environment (e.g., type of channelemployed, location), and the like. The contextual data can further besupplied by data banks, such as data mined by third parties—whereinvarious models such as: queuing models, Markov sources, probabilisticfunctions of Markov chains, and the like—can subsequently performprobabilistic predictions based upon the collected history. For example,if prior contacts exist with a customer/user—the subject innovation canmodel their specific behavior. Otherwise, a demographic model thatrepresents the behavior modeled as an average of previous contacts fromsimilar individuals can be employed. In one aspect, the actual decisionfor which model to employ, can initially be based on a weighted averageof the specific and demographic information—and subsequently modifiedwhen sufficient data is accumulated on the specific behavior, to obtainits accurate model.

Moreover, different pieces of information, such as collected history forcontact escalation, feasibility/cost criteria for a contact center,probabilistic/stochastic models; operating instructions, and the likecan also be maintained in the data storage systems 311, 312, 313. Suchdata storage systems can be a complex model based database structure,wherein an item, a sub-item, a property, and a relationship can bedefined to allow representation of information within a data storagesystem, for example. The data storage systems can further employ a setof basic building blocks for creating and managing rich, persistedobjects and links between objects—wherein an item can be defined as thesmallest unit of consistency within the data storage system, which canbe independently secured, serialized, synchronized, copied,backup/restored, for example.

As explained earlier, contact escalation periods can be employed as aperformance measure as part of a model that predicts and/or approximatequeuing scenarios based on mathematical analysis and/or stochasticprocedures. By analyzing of the relevant queuing model, cause ofpossible response delays or problems can be identified and impact ofproposed changes assessed. For example, different time periods for acontact escalation can be assigned various likelihood percentages—basedon how probable a user is to escalate a contact after expiration of suchpredetermined time periods. Specifically, training can occur through ann^(th)-order Markov model (where n is an integer) to probabilisticallypredict likelihood of contact escalations based on a short sequence ofaccumulated episodes of prior contact escalations, for example. It is tobe appreciated that any type of history that pertains to profilingcharacter traits associated with escalating a contact (e.g.,demographic, psychographic; behavioral information such as: level ofcustomer patience, satisfaction, tolerance; age, gender, wealth index,interests, shopping habits, and the like) can be employed as part ofpredicting the contact escalation.

The following discussion is an example for a Markov model that can beemployed in conjunction with aspects disclosed herein. It is to beappreciated that such discussion is exemplary in nature, and variousdeviations can occur from the particular model information. Predictionof a contact escalation period for a particular client or user of thecontact center can be based on its past history, wherein a model forsequence of contact escalation periods can be designated as x₁ wherein irepresents a continuous time variable. It is further to be appreciatedthat if the model is further simplified, i can alternatively represent adiscrete time variable. For example, considering an interval of t=3minute time frames from call initiation, wherein continuous timevariables can be represented as 0<x₁≦3 minutes, 3<x₂≦6 minutes; 6<x₃≦9minutes, and the like. Such model can further be simplified by definingdiscrete time points with jump discontinuities from call initiation,such as x₁=3 minutes; x₂=6 minutes; x₃=9 minutes; and the like.

Hence, x_(i) can represent a time period after call initiation by aparticular customer, and P[x_(i)] designates the likelihood ofescalation during (or for) such time period. Accordingly, for eachcustomer, whenever a prediction is performed, respective time/periodevaluated for contact escalation can be denoted as {x₀, x₁, x₂,x_(max)}, where x₀ is the time at initiation of the call, and x_(max)represents a predefined time period for the contact center that themodel no longer responds to the call, and instead implements otheractions such as appeasing the customer. Hence, depending on whether themodel employs a continuous probability distribution based on time as acontinuous random variable, or alternatively, is simplified as adiscrete probability distribution; the probability of contact escalationcan be represented by: P[x₁]=P[x_(i-1)<x≦x_(i)]—and predicted for; orduring such time periods.

Based on such model, a probability distribution can be obtained whereinthe horizontal axis represents discrete time points and the verticalaxis is the contact escalation likelihood. Associated histograms can bebuilt, and subsequently normalized to obtain a discrete or continuousprobability distribution. Moreover, various statistical values such asmean values and standard deviation can subsequently be computed. It isto be appreciated that parameters can further be defined based on secondorder models x_(ij), which can be sensitive to an additional element,such as particular channel information for a contact. Thus, models canbe built across inputs received by customers of the contact center,wherein the predicted escalation period can be considered as anadditional criterion for resource matching, which enableproactive/anticipatory contact routing.

FIG. 4 illustrates a particular system 400 that further employs anevaluation component 410 in conjunction with a queuing model(s) (1 thrup, p being an integer), which can employ contact escalation periods aspart of routing procedures (e.g., prioritization). A customer/userhistory can be collected and evaluated by the evaluation component 410,which analyzes history data regarding contact escalation. Moreover, acommunication module (not shown) can further be employed by theevaluation component 410 to engage with other devices/components, suchas for information exchange with the routing component 415, for example.Accordingly, likelihood percentages for contact escalation can be usedto perform a variety of tasks, such as satisfying priorities, matchingcustomers to contacts, and the like. A result of the evaluation and aMarkov Model can further be employed by the evaluation component, tocompute likelihood for contact escalation (e.g., percentage chance thata user escalates a contact from a less expensive resource to a moreexpensive resource of the contact center 405.)

Moreover, additional data can be collected that relate to comparing; thecosts of contacts actually escalating—versus—the costs of adding extraresources to handle the contacts for such class. For example, when asubstantial number of e-mail service requests are received by thecontact center, the model can estimate that 50% of such received e-mailswill escalate with the existing available set of resources. Accordingly,an estimated additional cost of “A” unit prices (arising from theescalation) is expected. Such additional cost of “A” unit prices, canthen be compared to the cost of adding additional contact center agentresources to the e-mail pool, having a cost of “B” unit prices—and yetwhich can reduce the escalation percentage of received e-mails to 5%,for example. As such, if A is larger than B (plus the escalation cost ofthe 5%), then it is cost beneficial for the contact center to add suchresources at the cost of “B” unit prices.

As illustrated, an instigation component 420 can further determine whenthe routing component 415 is to employ models for likelihood of contactescalation, based on various predetermined configurations (e.g., routingthresholds, sufficiency of accumulated history data). For example, therouting component 415 can initiate operation upon encountering contactsfrom particular customers, or when traffic of calls to the contactcenter 405 surpasses a pre-set number of contacts. Moreover, customerhistory related to contact escalation can be retained in a separatelocation, and thus a search component (not shown) can locate suchcontact escalation history. The search component can employ a pluralityof search criteria such as data related to: customer identification,contact type, channel employed, and the like.

Accordingly, different route matching and/or prioritizing outcomes forcontacts forwarded to the contact center 405 can occur, based in part onthe escalation likelihood. For example, a likelihood of escalation canbe predicted (e.g., simulated) for a customer with a history of contactsto a contact center, wherein if response to an e-mail of such user isdelayed for 22 hrs—the probability of contact escalation is determinedto be 80%. As such, the contact center 405 can determine if resourcesshould be matched to the customer before expiration of the 22 hrs timeperiod—to mitigate such likelihood of contact escalation. To this end,these likelihoods can be used in conjunction with differentfunctionalities, and different outcomes can take place regarding choiceof the contact escalation.

Moreover, if an outcome has a likelihood over a specific threshold, acheck can subsequently be performed to verify if risks exist that shouldbe considered when routing contacts and allocating resources. Managementof the contact center can hence be monitored to reach desired outcomesbased on prediction of contact escalation.

FIG. 5 illustrates another aspect of the subject innovation that can beemployed as part of monitoring a social network 510. Generally, thesocial network 510 can be described as a structure of nodes 511 thatrepresent individuals or groups of individuals (e.g., organizations).Social networking can also refer to a category of network applicationsthat facilitate connecting friends, business partners, or other entitiesor groups of entities together. In one aspect, the contact 515 can beinitially presented to the social network 510 (e.g., a peer-to-peergroup) and prior to the customer 525 actually forwarding such contact tothe contact center 550. The social network 510 can enhance searches viaendorsements by other entities (e.g., individual or groups ofindividuals) of the network, and/or can be maintained by businessentities that are associated with the contact center 550. Accordinglyand by monitoring the social network 510, the prediction component 560can further consider transfer of the contact from the social network 510to the contact center, as part of predicting the possibility forescalation.

As illustrated in FIG. 5, the system 500 can monitor interaction dataand search queries, presented by a customer as well as the queryresults, which are supplied by other members of such social network 510.The system 500 can include database 502 that stores mapping informationthat maps customer/user interaction information (e.g., search-relatedinformation) to an entity of the social network 510. The mappinginformation can be continuously updated and reorganized as links withinthe system mapping become stronger or weaker.

A monitor component 508 (which can include a search engine for searchingqueries) can monitor search queries submitted by the customer 525, anddetermine if such queries have been satisfactorily responded to. If not,the prediction component 560 of the contact center 550 can predictlikelihood that such search query will be escalated to a contact withthe contact center 550 in the future. By predicting when the customer525 is likely to escalate a contact from the social network 510 to thecontact center 550, anticipatory resource management can be initiated toobtain optimum results.

FIG. 6 illustrates a related methodology 600 of predicting when acustomer is likely to escalate a contact that is forwarded to a contactcenter, and employ such escalation prediction as part of managingrouting procedures (e.g., resource matching) for the contact center.While this exemplary method is illustrated and described herein as aseries of blocks representative of various events and/or acts, thesubject innovation is not limited by the illustrated ordering of suchblocks. For instance, some acts or events may occur in different ordersand/or concurrently with other acts or events, apart from the orderingillustrated herein, in accordance with the invention. In addition, notall illustrated blocks, events or acts, may be required to implement amethodology in accordance with the subject innovation. Moreover, it willbe appreciated that the exemplary method and other methods according tothe innovation may be implemented in association with the methodillustrated and described herein, as well as in association with othersystems and apparatus not illustrated or described.

Initially and at 610 feature extraction can be performed on a contactreceived by the contact center. The feature extraction can obtain datathat is associated with the contact, such as topic of request, customerinformation and the like. Such information extracted from the contactcan be represented in form of vectors for interaction with a model,generated at 620 that probabilistically predicts escalation point (e.g.,training of a Markov model). A customer history can further be createdor updated, wherein the Markov model can then compute an escalationlikelihood—(e.g., percentage chance that a customer will follow-up onthe initial request through another channel that is considered to bemore expensive from standpoint of the contact center)—as a function ofcharacter traits of customer, or cost of resources associated with thecontact center. For example, based upon user history, a Markov model canbe used to determine how likely the user is to make each decision. Forinstance, x % (x being a real number 0≦x≦100) can be the probability ofcontact escalation after a duration of “t” seconds (where t is a realnumber.)

Subsequently and at 630, a determination is made to verify whether acontact received by the contact center can be satisfactorily respondedto (e.g., prior to expiration of the escalation period). If not, byengaging in activities that appease or console the customer at 635—themethodology 600 mitigates or avoids contact escalation. Such activitiescan include, supplying an incentive in exchange for the customeragreeing to prolong the escalation period for a predetermined period oftime. Otherwise, and by act 640 the contact received by the contactcenter can be routed based on pattern matching that employ the predictedlikelihood for escalation of a contact. Subsequently, and based on suchlikelihood of escalation, priorities can be satisfied and patternsmatched, to avoid or mitigate actual occurrence of the escalation.

FIG. 7 illustrates a related methodology 700 of employing contactescalation periods as part of routing management for a contact center.Initially and at 710, history of contact escalation periods can beaccumulated based on information associated with a contact center suchas, customer information, subject matter of the contact, and the like.Subsequently, and at 720 the history of escalation times can beincorporated as part of a model that facilitates prediction of contactescalation (e.g., such incorporation can relate to training of themodels.) The models can subsequently be employed as part of the routingsystems for the contact center at 730. As such, the contact center canroute/prioritize inbound calls based at least in part on the predictedescalation periods via the model. Subsequently and at 740, the model canbe dynamically updated and further trained based on new/most recent dataand information obtained from the contact center.

FIG. 8 illustrates an inference component 830 (e.g., an artificialintelligence—AI) that can facilitate inferring and/or determining when,where, how to predict contact escalation data and/or train queuing modelthat employ contact escalation as an additional parameter in accordancewith an aspect of the subject innovation. As used herein, the term“inference” refers generally to the process of reasoning about orinferring states of the system, environment, and/or user from a set ofobservations as captured via events and/or data. Inference can identifya specific context or action, or can generate a probability distributionover states, for example. The inference can be probabilistic—that is,the computation of a probability distribution over states of interestbased on a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources.

The inference component 830 can employ any of a variety of suitableAI-based schemes as described supra in connection with facilitatingvarious aspects of the herein described invention. For example, aprocess for learning explicitly or implicitly how parameters are to becreated for training models based on escalations of contact can befacilitated via an automatic classification system and process.Classification can employ a probabilistic and/or statistical-basedanalysis (e.g., factoring into the analysis utilities and costs) toprognose or infer an action that a user desires to be automaticallyperformed. For example, a support vector machine (SVM) classifier can beemployed. Other classification approaches include Bayesian networks,decision trees, and probabilistic classification models providingdifferent patterns of independence can be employed. Classification asused herein also is inclusive of statistical regression that is utilizedto develop models of priority.

As will be readily appreciated from the subject specification, thesubject innovation can employ classifiers that are explicitly trained(e.g., via a generic training data) as well as implicitly trained (e.g.,via observing user behavior, receiving extrinsic information) so thatthe classifier is used to automatically determine according to apredetermined criteria which answer to return to a question. Forexample, SVM's can be configured via a learning or training phase withina classifier constructor and feature selection module. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, xn), toa confidence that the input belongs to a class—that is,f(x)=confidence(class).

In one aspect, such “training” data can include specific “abandon”events. For example, in context of a voice contact, an “abandon” eventcan be identified when the telephone switching equipment detects that acaller has placed the phone on hook—and hence has dropped the call. Forother channels, abandon events can be determined via different methods,such as by identifying when a “new” contact by the same user, isreplacing a previous (and not yet satisfied) contact by that same user.Likewise, in text chat message environments, a “session” can bemaintained based on a connection that is similar to a telephone contact,and which provides a positive indication that the user has abandoned thecontact attempt, for example. Accordingly, various forms of trainingdata for abandoned events can be accumulated, which further enhanceaccuracy of the procedures related to the subject innovation.

FIG. 9 illustrates a system 900 that includes a component 902 forpredicting when a customer is likely to escalate a contact that isforwarded to a contact center (e.g., a logical grouping of entities thatrepresent means for predicting contact escalation.) The system 900further includes logical grouping of entities for routing an inboundcontact based on such predicted contact escalation periods, andrepresented as component 904 (e.g., means for routing contacts). Inaddition, component 906 can represent a component for analyzing relevantqueuing models, to identify cause of queuing issues and assess impact ofproposed changes (e.g., means for analyzing.) The system 900 can residepartially within a contact center and/or operationally connectedtherewith. Moreover, the system 900 can include a memory 910 thatretains instructions for executing functions associated with components902, 904 and 906. While shown as being external to memory 910, it is tobe understood that components 902, 904, 906 can exist within memory 910.

The system 900 enables contact escalation periods to be employed as aperformance measure for a model that predicts and/or approximatesqueuing scenarios, based on mathematical analysis and/or stochasticprocedures. By analyzing the relevant queuing model, potential problemsrelated to queuing can be identified and impact of proposed changesassessed.

As used in this application, the terms “component,” “module,” “engine,”“system,” and/or functions associated therewith can be facilitated by acomputer-related entity, such as hardware, firmware, software, softwarein execution; processor, computer readable medium; or combinationthereof. For example, a component can be, but is not limited to being, aprocess running on a processor, a processor, an object, an executable, athread of execution, a program, and/or a computer.

By way of illustration, both an application running on a computingdevice and/or the computing device itself can represent a component. Oneor more components can reside within a process and/or thread ofexecution and a component can be localized on one computer and/ordistributed between two or more computers. In addition, these componentscan execute from various computer readable media having various datastructures stored thereon. In another example, the computer-readablemedium can include various sets of codes for causing a computer tointeract with information indicative of states associated with a systemthat implements the subject innovation. The components can communicateby way of local and/or remote processes such as in accordance with asignal having one or more data packets (e.g., data from one componentinteracting with another component in a local system, distributedsystem, and/or across a network such as the Internet with other systemsby way of the signal).

Any aspect or design described herein as “exemplary” is not necessarilyto be construed as preferred or advantageous over other aspects ordesigns. Similarly, examples are provided herein solely for purposes ofclarity and understanding and are not meant to limit the subjectinnovation or portion thereof in any manner. It is to be appreciatedthat a myriad of additional or alternate examples could have beenpresented, but have been omitted for purposes of brevity.

Furthermore, all or portions of the subject innovation can beimplemented as a system, method, apparatus, or article of manufactureusing standard programming and/or engineering techniques to producesoftware, firmware, hardware or any combination thereof to control acomputer to implement the disclosed innovation. For example, computerreadable media can include but are not limited to magnetic storagedevices (e.g., hard disk, floppy disk, magnetic strips . . . ), opticaldisks (e.g., compact disk (CD), digital versatile disk (DVD) . . . ),smart cards, and flash memory devices (e.g., card, stick, key drive . .. ). Additionally it should be appreciated that a carrier wave can beemployed to carry computer-readable electronic data such as those usedin transmitting and receiving electronic mail or in accessing a networksuch as the Internet or a local area network (LAN). Of course, thoseskilled in the art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of the claimedsubject matter.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 10 and 11 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented. While the subject matter has been described above inthe general context of computer-executable instructions of a computerprogram that runs on a computer and/or computers, those skilled in theart will recognize that the innovation also may be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinnovative methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, mini-computing devices, mainframe computers, as well aspersonal computers, hand-held computing devices (e.g., personal digitalassistant (PDA), phone, watch . . . ), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects may also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. However, some, if not allaspects of the innovation can be practiced on stand-alone computers. Ina distributed computing environment, program modules may be located inboth local and remote memory storage devices.

With reference to FIG. 10, an exemplary environment 1010 forimplementing various aspects of the subject innovation is described thatincludes a computer 1012. The computer 1012 includes a processing unit1014, a system memory 1016, and a system bus 1018. The system bus 1018couples system components including, but not limited to, the systemmemory 1016 to the processing unit 1014. The processing unit 1014 can beany of various available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as the processing unit1014.

The system bus 1018 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, 11-bit bus, IndustrialStandard Architecture (ISA), Micro-Channel Architecture (MSA), ExtendedISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Universal Serial Bus (USB),Advanced Graphics Port (AGP), Personal Computer Memory CardInternational Association bus (PCMCIA), and Small Computer SystemsInterface (SCSI).

The system memory 1016 includes volatile memory 1020 and nonvolatilememory 1022. The basic input/output system (BIOS), containing the basicroutines to transfer information between elements within the computer1012, such as during start-up, is stored in nonvolatile memory 1022. Byway of illustration, and not limitation, nonvolatile memory 1022 caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable ROM (EEPROM), or flashmemory. Volatile memory 1020 includes random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM).

Computer 1012 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 10 illustrates, forexample a disk storage 1024. Disk storage 1024 includes, but is notlimited to, devices like a magnetic disk drive, floppy disk drive, tapedrive, Jaz drive, Zip drive, LS-60 drive, flash memory card, or memorystick. In addition, disk storage 1024 can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage devices 1024 to the system bus 1018, aremovable or non-removable interface is typically used such as interface1026.

It is to be appreciated that FIG. 10 describes software that acts as anintermediary between users and the basic computer resources described insuitable operating environment 1010. Such software includes an operatingsystem 1028. Operating system 1028, which can be stored on disk storage1024, acts to control and allocate resources of the computer system1012. System applications 1030 take advantage of the management ofresources by operating system 1028 through program modules 1032 andprogram data 1034 stored either in system memory 1016 or on disk storage1024. It is to be appreciated that various components described hereincan be implemented with various operating systems or combinations ofoperating systems.

A user enters commands or information into the computer 1012 throughinput device(s) 1036. Input devices 1036 include, but are not limitedto, a pointing device such as a mouse, trackball, stylus, touch pad,keyboard, microphone, joystick, game pad, satellite dish, scanner, TVtuner card, digital camera, digital video camera, web camera, and thelike. These and other input devices connect to the processing unit 1014through the system bus 1018 via interface port(s) 1038. Interfaceport(s) 1038 include, for example, a serial port, a parallel port, agame port, and a universal serial bus (USB). Output device(s) 1040 usesome of the same type of ports as input device(s) 1036. Thus, forexample, a USB port may be used to provide input to computer 1012, andto output information from computer 1012 to an output device 1040.Output adapter 1042 is provided to illustrate that there are some outputdevices 1040 like monitors, speakers, and printers, among other outputdevices 1040 that require special adapters. The output adapters 1042include, by way of illustration and not limitation, video and soundcards that provide a means of connection between the output device 1040and the system bus 1018. It should be noted that other devices and/orsystems of devices provide both input and output capabilities such asremote computer(s) 1044.

Computer 1012 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)1044. The remote computer(s) 1044 can be a personal computer, a server,a router, a network PC, a workstation, a microprocessor based appliance,a peer device or other common network node and the like, and typicallyincludes many or all of the elements described relative to computer1012. For purposes of brevity, only a memory storage device 1046 isillustrated with remote computer(s) 1044. Remote computer(s) 1044 islogically connected to computer 1012 through a network interface 1048and then physically connected via communication connection 1050. Networkinterface 1048 encompasses communication networks such as local-areanetworks (LAN) and wide-area networks (WAN). LAN technologies includeFiber Distributed Data Interface (FDDI), Copper Distributed DataInterface (CDDI), Ethernet/IEEE 802.3, Token Ring/IEEE 802.5 and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL).

Communication connection(s) 1050 refers to the hardware/softwareemployed to connect the network interface 1048 to the bus 1018. Whilecommunication connection 1050 is shown for illustrative clarity insidecomputer 1012, it can also be external to computer 1012. Thehardware/software necessary for connection to the network interface 1048includes, for exemplary purposes only, internal and externaltechnologies such as, modems including regular telephone grade modems,cable modems and DSL modems, ISDN adapters, and Ethernet cards.

FIG. 11 is a schematic block diagram of a sample-computing environment1100 that can be employed for predicting likelihood for contactescalations. The system 1100 includes one or more client(s) 1110. Theclient(s) 1110 can be hardware and/or software (e.g., threads,processes, computing devices). The system 1100 also includes one or moreserver(s) 1130. The server(s) 1130 can also be hardware and/or software(e.g., threads, processes, computing devices). The servers 1130 canhouse threads to perform transformations by employing the componentsdescribed herein, for example. One possible communication between aclient 1110 and a server 1130 may be in the form of a data packetadapted to be transmitted between two or more computer processes. Thesystem 1100 includes a communication framework 1150 that can be employedto facilitate communications between the client(s) 1110 and theserver(s) 1130. The client(s) 1110 are operably connected to one or moreclient data store(s) 1160 that can be employed to store informationlocal to the client(s) 1110. Similarly, the server(s) 1130 are operablyconnected to one or more server data store(s) 1140 that can be employedto store information local to the servers 1130.

What has been described above includes various exemplary aspects. It is,of course, not possible to describe every conceivable combination ofcomponents or methodologies for purposes of describing these aspects,but one of ordinary skill in the art may recognize that many furthercombinations and permutations are possible. Accordingly, the aspectsdescribed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims.

Furthermore, to the extent that the term “includes” is used in eitherthe detailed description or the claims, such term is intended to beinclusive in a manner similar to the term “comprising” as “comprising”is interpreted when employed as a transitional word in a claim.

1. A contact center comprising: a prediction component that predictslikelihood of an escalation for a contact, the escalation represented byabandonment of an initial request—in favor of—an additional requestrequiring a resource of the contact center that is more expensive than aresource required by the initial request; and a routing component thatroutes the contact based on likelihood of the escalation.
 2. The contactcenter of claim 1 further comprising an analysis component that performscost-benefit analysis for contacts and resources of the contact center.3. The contact center of claim 1 further comprising a queuing modelgenerated based in-part on likelihood of the escalation for the contact.4. The contact center of claim 1, the escalation is represented by thefollow-up of the initial request, via a channel that is more expensivethan an original channel that the initial request is sent thereby to thecontact center.
 5. The contact center of claim 1, the predictioncomponent is associated with a monitor component that monitors a socialnetwork.
 6. The contact center of claim 5, the monitor component furthercomprises a search engine that searches for queries submitted to thesocial network.
 7. The contact center of claim 1 further comprising anevaluation component that evaluates history of a customer, to facilitateprediction of contact escalation.
 8. The contact center of claim 7further comprising an instigation component that initiates operation forthe routing component.
 9. The contact center of claim 1 furthercomprising an artificial intelligence component that facilitatesprediction of contact escalation.
 10. A method of routing a contact in acontact center, comprising: predicting an escalation likelihood for acontact of the contact center via a prediction component; and routingthe contact based on the escalation likelihood.
 11. The method of claim10 further comprising prioritizing the contact based on the escalationlikelihood.
 12. The method of claim 10, the predicting act is based onsubject matter of the contact, or character traits of a customergenerating the contact, or contact environment, or a combinationthereof.
 13. The method of claim 10, the contact submitted to a socialnetwork before received by the contact center.
 14. The method of claim10 further comprising engaging in remedial measures for appeasing acustomer who submitted the contact, in exchange for prolonging anescalation period.
 15. The method of claim 10, the predicting act basedon employing queuing models with Markov chains.
 16. The method of claim15 further comprising dynamically updating the queuing models.
 17. Themethod of claim 10 further comprising matching the contact to resourcesof the contact center based on the predicting act.
 18. The method ofclaim 10 further comprising facilitating the predicting act viainferences supplied by an inference component.
 19. A contact centercomprising: means for predicting likelihood of an escalation for acontact; and means for routing the contact based on likelihood of theescalation.
 20. The contact center of claim 19 further comprising meansfor analyzing cost-benefits for the contact center.